The disclosed embodiments of the present invention relate to the stereo video/image playback, and more particularly, to an auto-convergence system with active learning and related method and machine-readable medium thereof.
With the development of science and technology, users are pursing stereo and more real image displays rather than high quality images. There are two techniques of present stereo display. One is to use a display apparatus, which collaborates with glasses (such as anaglyph glasses, polarization glasses or shutter glasses), while the other one is to use only a display apparatus without any accompanying glasses. No matter which technique is utilized, the main theory of stereo image display is to make the left eye and the right eye see different images, thus the brain will regard the different images seen from two eyes as a stereo image.
In general, the disparity of an object/pixel presented in a stereo image pair composed of a left-view image and a right-view image determines user's depth perception of the object/pixel. However, if the disparity is not properly set, the user may suffer from visual fatigue caused by, for example, vertical disparity error and/or vergence-accommodation conflict. To mitigate the visual fatigue, one possible solution is to make the depth perception comfortable to the user. One conventional design simply employs a fixed setting of the comfortable convergence range which is determined solely based on the specification of the stereo display apparatus such as a pre-defined vergence angle of a three-dimensional (3D) display panel. Unfortunately, such a fixed convergence range is unable to meet depth perception preferences of all users due to the fact that a subjective preference of depth perception is hard to be well defined for all users. For example, some users may prefer stronger perception of depth, while other users may prefer more comfortable visual effect. Besides, there may be a significant difference between theoretical and empirical settings of the comfortable convergence range for a user, and the depth perception of a stereo video/image may be content dependent. Hence, there is no general setting of the comfortable convergence range to satisfy all users. As a result, the conventional design is unable to make different users have best 3D viewing experiences under the same setting of the comfortable convergence range.
Thus, there is a need for an innovative design for an intelligent personalized auto-convergence scheme which is capable of making the comfortable convergence range adapted to the personal preference of each user.